Flawed multiple linear regression in academia? Heteroscedasticity's effect on p-value? I believe I have found a paper in academia that has used a flawed multiple linear regression. I have downloaded the data set and replicated their regression results. I have done some diagnostics and have found this to my surprise:

There clearly is heteroscedasticity in the model, right? Hence, this violates the assumption of MLR that there is homoscedasticity.
Thus far I have found that heteroscedasticity has an effect on p-value, i.e. that it makes p-values for independent variables' association with dependent variable smaller. Thus, with heterscedasticity, the MLR model can show significant relationships between IVs and DVs, when in reality the significance is absent.
Is my understanding correct? Any useful resources on what heteroscedasticity entails for the MLR model's results?
Appreciate it.
 A: This isn't heteroscedasticity you are looking at, but truncation.
You can see this very clearly in the first plot: No combination of the fitted + residual exceeds a certain number, causing this sudden imaginary diagonal line, past which no observations exist. In the scale-location plot, this strange shape reveals that the data are truncated at $1$.
It is easy to simulate some truncated data and show that the diagnostic plots indeed display this diagonal cutoff, as well as the strange V-shape in the scale-location plot:
set.seed(1234)
n      <- 1000
beta_0 <- 1.5
beta_1 <- 0.5
x      <- rnorm(n)
y      <- beta_0 + beta_1 * x + rnorm(n, 0, 0.5)
y      <- pmin(y, 1)
plot(lm(y ~ x))


The real question isn't what to conclude from these diagnostic plots, but rather what these data are. If you include a reference to the paper you read, we could see why the data are bounded, and whether that renders their conclusions invalid or not.

Edit: In the comments you explained these are ratios. That gives you the actual answer to whether their approach is flawed (it probably is). Rather than an ordinary linear model, the authors should probably have used e.g. logistic regression using the original values that made up these ratios.
